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Morten Jerven.Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It.
Cornell Studies in Political Economy Series. Ithaca: Cornell University Press, 2013. 176 pp.
$65.00 (cloth), ISBN 978-0-8014-5163-8; $22.95 (paper), ISBN 978-0-8014-7860-4.

Reviewed by Jennifer De Maio (California State University, Northridge)Published on H-Diplo (October, 2013)Commissioned by Seth Offenbach

Why Do We Need Better Numbers?

How reliable are the statistics we use to study the development of African countries and why does the reliability of these statistics matter? These are the central questions addressed by Morton Jerven, an economic historian, in his book Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It. One of the most significant contributions of his work is the systematic way that he approaches the issue of how economic data is produced and consumed. The quality of numbers has important implications for the assumptions we make, the resources we seek, and the policies we advocate for political and economic development in sub-Saharan Africa.

Scholars of African economies have long known that much of the data coming from statistics offices in the region have been inaccurate. The extent and nature of those inaccuracies, however, and the subsequent impact that they have on the welfare of citizens in developing countries have not been sufficiently assessed. Jerven fills a gap in the literature by historicizing and contextualizing national income accounting in Africa. He examines how economic statistics in African countries are produced and considers how problems with the quality of data can lead to controversies and disagreements between policymakers, scholars, and nongovernmental organizations. Jerven supports his arguments with survey information and interviews conducted at various statistical offices in sub-Saharan Africa.

Jerven’s book is clear, concise, and well reasoned. The analysis is based on four years of research looking at how African countries obtain and disseminate their data. He argues that the low quality of this data has an effect on the quality of the conclusions that the development community has reached based on these numbers. Jerven considers the gross domestic product (GDP) estimates in particular and argues that it has been difficult for African countries to provide accurate income measures because of the presence of large subsistence economies and unrecorded transactions. What these measurement problems imply is that estimates of GDP can be grossly under- or overestimated. Jerven also posits that statistics offices frequently do not update their reporting and so they may miss important changes in economic activity, such as increases in the relevance of the cell phone sector.

Jerven studies the nature of data sets and asserts that differences in how we calculate GDP can result in changes in how we use the data. He explores three commonly used data sets for GDP: World Development Indicators, the Penn World Table, and the Maddison Project. While these sources collect the same data, they calculate inflation and other variables differently. Thus, their economic rankings of countries can differ significantly. The numbers in these data sets matter for several reasons. Western determination of where and how much development assistance to allocate depends on an assessment of a country’s economic health as signaled by GDP estimates. Because of variations in the data sets and because of problems obtaining accurate data, it may be difficult to get an accurate sense of whether a country’s economy is growing or shrinking over a particular period. What these measurement variations imply is that it is hard to determine with any degree of certainty whether one country’s GDP is lower than another’s. Donors could be allocating resources to one country when its neighbor is in fact facing a far bleaker economic situation. In addition to the problems with allocating resources, we may be drawing the wrong conclusions about the types of economic policies that stimulate or hinder development.

Jerven quotes Albert Einstein and writes: “Not everything counts that can be counted; and not everything that can be counted, counts” (p. 111). How economists count and how they use what they count is the central theme of Jerven’s book. One of the potential criticisms of his analysis is that in focusing on numbers, the development community may lose sight of other factors, like institutions. Is the poor quality of numbers that have been coming out of Africa that matters or is should the poor institutions that contribute to measurement problems be the primary concern for the development community? Jerven would argue that the two are not mutually exclusive. Institutions matter and are one cut at the problem. But what Jerven’s research contributes to the debate is a careful examination of the limitations of the numbers that we rely on to justify policies. He is not making a choice between numbers and institutions; instead, he seeks to bridge the gap between scholars and policymakers who use quantitative and qualitative methods and posits that a better understanding of where statistics for African countries come from will improve the overall quality of the conclusions that we draw.

One of the policy implications of Jerven’s work is that development resources need to be directed toward supporting national statistics offices across Africa so that they can produce better data about their economies. A potential counter argument could be made that instead of supporting the statistics offices, the development community should be focusing on the citizens themselves and devoting as much development assistance directly to them as possible. Getting the GDP numbers right, however, may be the necessary first step in determining who has the greatest need for investments in health and development.

The most important lesson that scholars and policymakers should take away from Poor Numbers is that academics and policymakers need to be more responsible in how we use statistics. We need to understand how the numbers we are using were generated and the limitations that the data may have. It is only with this awareness that we can increase confidence in our analyses and the policies that we advocate.